Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments

2026-07-31

AI Summary

This work addresses critical challenges faced by large language models in real-time, regulated environments—namely outdated knowledge, catastrophic forgetting, hallucination, and weak feedback loops—by proposing a unified pattern-driven LLMOps architecture that enables an end-to-end operational pipeline. The core innovations include an Adaptive Ingestion and Pipeline Orchestrator (AIPO), a STAR+FAR continual learning mechanism, an SLO-aware adaptive retrieval strategy (SAGE), and an RLHF triggering phase that automatically converges based on feedback. By integrating real-time data ingestion, sparse temporal adapter routing, freshness-aware replay, and retrieval-augmented generation, the architecture substantially mitigates the trade-offs among latency, cost, and accuracy while providing the auditability and rollback capabilities required in high-stakes domains such as healthcare and finance.

Abstract

Large language models deployed in real-time, regulated settings face knowledge staleness, catastrophic forgetting, hallucination, and weak feedback loops. We present a unified, pattern-driven LLMOps architecture integrating real-time data ingestion, continual learning, retrieval-augmented generation (RAG), and human-in-the-loop feedback into a single operational pipeline. Four contributions map to established software design patterns: an adaptive ingestion pattern orchestrator (AIPO) evaluated with FreshStreamBench; STAR+FAR continual learning with sparse temporal adapter routing and freshness-aware replay; SAGE, an SLO-aware adaptive retrieval policy predicting a per-query passage budget to meet tail-latency targets; and an automated feedback-driven convergence stage with RLHF triggers. The result reduces latency-cost-accuracy trade-offs while supporting auditability and rollback for high-risk sectors such as health care and finance.

Problem

• knowledge staleness
• catastrophic forgetting
• hallucination
• weak feedback loops
• real-time deployment

Innovation

• LLMOps
• continual learning
• retrieval-augmented generation
• human-in-the-loop
• adaptive retrieval